Simulating the connections of ENSO and the hydrology of the Blue Nile using a climate model of the tropics

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Introduction
The Blue Nile has a great impact on the life of millions of people in Ethiopia, Sudan, and Egypt.It originates from Lake Tana and descends from the Ethiopian high plateau, with many tributaries in its upper course across the Ethiopian Highlands and two more tributaries (Dinder and the Rahad) in its lower course across Sudan.Although the Blue Nile is relatively short compared to the White Nile and it has a relatively small drainage area, it carries 60-70 % of the total discharge and a great amount of sediment (Dumont, 2009).
During the last few decades, there has been a wide recognition that natural oscillations in the state of the Pacific Ocean have a significant impact on the patterns of weather and climate around the world.The dominant among these oscillations is known as the El Niño -Southern Oscillation (ENSO) which has a period of about Introduction

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Full Pacific as well as in remote regions around the globe (e.g.Müller and Roeckner, 2008).Eltahir (1996) found that 25 % of the natural variability in the annual flow of the Nile is associated with ENSO and proposed to use this observed correlation to improve the predictability of the Nile floods.Wang and Eltahir (1999) recommended an empirical methodology for medium and long-range (∼6 months) forecasting of the Nile floods using ENSO information, while Amarasekera et al. (1997) showed that ENSO episodes are negatively correlated with the floods of the Blue Nile and Atbara rivers which originate in Ethiopia.In addition, Eltahir (1996) showed that the probability of having a low (high) flood given cold SST conditions is 2 % (49 %), while the probability of having a high (low) flood given a warm SST condition is 8 % (58 %).
Some recent studies divided Ethiopia into regions according to the variability of rainfall seasonality and used observational datasets to study the impact of sea surface temperature (SST) on the rainfall in the Ethiopian Highlands (Segele and Lamb, 2005;Seleshi and Zanke, 2004;Gissila et al., 2004).Other studies concentrated on East Africa but in regions outside the Ethiopian Highland, and showed negative correlation with ENSO in central and southern Sudan (Osman et al., 2001;Elagib and Elhag, 2011;Osman and Shamseldin, 2002).
These previous studies were conducted using observational datasets of SST, rainfall and river flow.In this study, we assess whether these observed connections between droughts/floods in the Blue Nile region and SSTs in the Pacific Ocean can be reproduced by using a physically based model of the climate system (the Regional Climate Model RegCM4 in its tropical band configuration, RegTB; Giorgi et al., 2012;Coppola et al., 2012).Towards this purpose we completed and analysed an ensemble of 9 simulations of tropical climate driven by observed SSTs and north-south boundary conditions from the ERA-Interim reanalysis of observations for the 28-yr period 1982-2009.In particular, we focus on the impact of ENSO on drought and flood conditions in the upper catchment of the Blue Nile by comparing simulation results with available discharge and precipitation observations.Introduction

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Full In Sect. 2 we describe model and experiment design, while in Sect. 3 we validate the model climatology over the Africa region of interest in this study.In Sect. 4 we then assess the representation of the connections between ENSO and the flood/drought conditions in the Blue Nile river basin.Final considerations are then provided in Sect. 5.

REGCM4 description, experiments and data
RegCM4 is described by Giorgi et al. (2012).It is an evolution of the previous version, RegCM3, described by Pal et al., (2007), with multiple physics options.Among the new developments of RegCM4 is the tropical band configurations described by Coppola et al. (2012), or Reg-TB, by which the model domain covers the entire tropical belt with periodic boundary conditions in the x direction and relaxation boundary conditions at the southern and northern boundaries.Figure 1 shows the model domain and topography along with some sub-regions selected for more detailed regional analysis.For our experiments the horizontal resolution is approximately 125 km at the equator, i.e. 360 grid points.In the north-south direction the domain extends to about +/−45 degrees and 18 sigma layers are used in the vertical, as in its standard configuration.The red rectangle in the Pacific Ocean represents the Nino 3.4 region, whereas the small square over Ethiopia represents out study area, i.e. the catchment of the upper Blue Nile.
For our experiment we used the following physics options described in Giorgi et al. (2012): modified CCM3 radiative transfer scheme (Kiehl et al., 1996), modified (Holtslag et al., 1990) planetary boundary layer scheme, SUBEX resolvable precipitation scheme (Pal et al., 2000), mixed cumulus convection configuration utilizing the scheme of Grell (1993) over land and that of (Emanuel, 1991) over oceans and the biosphere-atmosphere transfer scheme (Dickinson et al., 1993)  the ERA-Interim 1.5  Mitchell et al., 2004) is also used for more detailed regional analysis.

Validation of model climatology over East Africa
In this section we first present a basic validation of the model climatology during the June-July-August-September (JJAS) rainy season over the broad east Africa region by comparing averages taken over the entire nine member ensemble with available observations.Note that this model configuration is the same as that used by Coppola et al. (2012), and in that paper an assessment is provided of the model climatology over the entire tropical belt.In this regard, Coppola et al. (2012) show that, although regional biases in the model are present, the basic model climatology of equatorial and tropical regions is realistic.We also recall that, although we only analyse here data from the African region, the model is run for the full tropical band with forcing only at the northern and southern boundary, and therefore it is quite free to develop its own regional circulations within this large domain.

Rainfall
The spatial pattern of JJAS rainfall is compared to observations (both CRU and GPCP) in Fig. 2. In JJAS the ITCZ is located in the northern hemisphere, therefore rainfall over the continent is mostly confined between 7 • S and 18 • N, while regions above and Introduction

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Full below these latitudes are relatively dry.This pattern of rainfall is mainly associated with the occurrence of mesoscale convective systems (d'Amato and Lebel, 1998;Jenkins et al., 2005).Two rainfall maxima are located around the Cameroon Mountains and Ethiopian Highlands which are associated with local orographic features.The model captures the general patterns of the observed rainfall distribution, in particular the ITCZ position and intensity.However, rainfall over southern Sudan, Central Africa and the Ethiopian Highlands is overestimated.Also, the monsoon rain belt appears narrower in the model than in the observational datasets, as indicated by the negative precipitation biases north and south of the main rain belt.In general, the performance of our Reg-TB configuration appears in line with previous studies performed using the RegCM system in various configurations and domains (Sun et al., 1999;Pal et al., 2007;Anyah and Semazzi, 2007;Sylla et al., 2010b;Steiner et al., 2009;Zaroug et al., 2013) or other regional modeling systems (e.g., Vizy and Cook, 2002;Nikulin et al., 2012;Paeth et al., 2005;Gallée et al., 2004;Flaounas et al., 2011;Druyan et al., 2008).

Temperature
The seasonal average of JJA 2-m temperature for 1982-2009 is compared to CRU observations in Fig. 3.The lowest temperatures are found mostly over the mountainous areas of Cameroun and Ethiopian Highlands, Tanzania and south Kenya, while the warmest areas are confined between 15 and 27 • N, with larger values over the Sahara desert.Reg-TB (Fig. 3a) reproduces this spatial pattern but it shows a systematic cold bias of a few degrees in the convective regions in East Africa (Fig. 3b), Nigeria, Algeria, and Libya.This cold bias over tropical and equatorial Africa has been a persistent feature in RegCM, as also found for example in the experiments of Sylla et al. (2010b) and Coppola et al. (2012), although the magnitude of the bias is somewhat reduced in our simulation.It should be stressed that the CRU observations are possibly affected by large uncertainties in this region due to the lack of observing stations, particularly in remote and mountainous areas (Mitchell et al., 2004).In addition, surface temperature depends on many parameters, including the presence of dust and aerosols.Given the Introduction

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Full uncertainty about these variables and that we do not include such processes in the model, we assess that a model bias of a few degrees is acceptable for this study.

Outgoing Longwave Radiation (OLR)
Figure 4 compares simulated and observed (NOAA) averaged JJA OLR, which is essentially a measure of cloudiness.Both the model (Fig. 4a) and the observations (not shown) exhibit larger OLR values in North Africa and south of 5 • S because of small amounts of cloud cover, with correspondingly low OLR over the ITCZ.The model tends to overestimate the OLR over the Congo basin as a result of an underestimation of clouds over this region, a result which is in line with previous applications of thee model (Zaroug et al., 2013;Sylla et al., 2010a, b).Slight overestimates are found over the Sahara and the greater horn of Africa, but in general the model captures well the general observed features of the OLR pattern in both magnitude and spatial extent.

Low level circulation, Tropical Easterly Jet (TEJ) and African Easterly Jet (AEJ)
The spatial patterns of average JJA low level (925 mb) circulation are shown in The TEJ develops between 200 and 150 mb in the upper troposphere over India in response to a large meridional thermal gradient and settles during the northern summer Asian monsoon season between the Tibetan Highlands and the Indian Ocean (Fontaine and Janicot, 1992;Koteswaram, 1958;Chen and van Loon, 1987).Introduction

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Full Atlantic (Wu et al., 2009), and it is linked to anomalous SSTs on a planetary scale (Chen and van Loon, 1987).The TEJ is one of the planetary features that affect the north African summer climate variability (Chen and van Loon, 1987).Figure 6a shows the TEJ in the ERA Interim reanalysis confined between 3 and 17 • N with a core speed exceeding 15 m s −1 .The band of the jet decreases gradually from East Africa to West Africa.In fact, the highest wind speed of about 18 m s −1 occurs over the Horn of Africa and the western Indian Ocean, while the lowest values of about 6 m s −1 are found over Niger.RegTB reproduces well the structure of the TEJ shown in the ERA Interim reanalysis, capturing both the location and intensity of the jet, although the core of the jet extends somewhat westward and further into Sudan compared to ERA-Interim.
The AEJ results mainly from the vertical inversion (around 600-700 mb) of the meridional temperature gradient between the Sahara and equatorial Africa due to the existence of strong surface baroclinicity (Cook, 1999;Steiner et al., 2009) associated with atmospheric deep convection (Thorncroft and Blackburn, 1999;Sylla et al., 2010b).Figure 6c shows the ERA Interim zonal wind in JJA at 600 mb.It is confined approximately between 7 and 20 • N extending from Chad to the Atlantic Ocean with a core speed ranging from 11 to 13 m s −1 located over West Africa.Again, RegTB simulates reasonably well both the strength and location of the AEJ (Fig. 6d).
In summary, the analysis presented in this section indicates that RegTB produces a realistic simulation of the climatology of the main dynamical and thermodynamical features of East African climate.In the next section we can thus turn our attention to the connections between ENSO and the hydroclimate of the upper Nile river basin.The GPCP data was analyzed for 28 yr , and then 5 La Niña years and 5 El Niño years included in this period were selected.The average precipitation for the 5 La Niña years (1988, 1998, 1999, 2007, and 2008), the 5 El Niño years (1982, 1983, 1987, 1992, and 2002) along with their difference are shown in the upper, middle and lower panels of Fig. 7, respectively.The GPCP data show a strong signal of increased rainfall during La Niña years over the Sahel region up to about 18 • N, including the upper catchment of the Nile river.This result agrees qualitatively with previous observational analyses, which suggested that La Niña years are associated with above normal rainfall and El Niño years with below normal rainfall in the upper catchment of the Blue Nile (Eltahir, 1996;Wang and Eltahir, 1999;Amarasekera et al., 1997;De Putter et al., 1998;Camberlin et al., 2001;Abtew et al., 2009).

Difference between La Niña and El Niño years
Figure 8 shows the corresponding fields for the 9 member ensemble of RegTB simulations.The 9 members were averaged over the 28 simulated years , and then 5 La Niña years and 5 El Niño years were selected as in the observational analysis.It can be seen that the model captures the positive La Niña minus El Niño precipitation signal over the Sahel region, although this is less intense than in the observations and does not extend as faar north.In addition, the models show a negative signal in the region just North of Lake Victoria.The results of each ensemble member were also analyzed (not shown) and they showed a remarkable variability between different members, which results in the main signal over the Sahel being weakened.Notwithstanding this problem, it appears that the model captured the main positive signal over the Sahel band and the upper Nile catchment.Figure 10 shows the time evolution of AMJ SST anomalies and ensemble average JJAS precipitation over the upper Blue Nile river catchment, further evidencing the negative correlation between these two variables.

Correlation between rainfall anomalies over the Ethiopian
In a recent study Zaroug et al. (2013) showed that the Blue Nile river discharge showed the highest correlation for drought events with the Nino 3.4 SST during AMJ, a result which appears to be in line with those of Figs. 9 and 10.These results support the use of Nino 3.4 SST during AMJ for seasonal forecasting of hydroclimate conditions in the upper catchment of the Blue Nile.While the correlations of Fig. 9 were calculated using the 9 member ensemble average, we also repeated the same exercise for each member separately (not shown) and found lower correlation values and a wide spread across members.This indicates that ensembles of simulations are needed to capture the atmospheric response to ENSO (Shukla et al., 2000).

Regression analysis of DJF and JJA Nino 3.4 index onto REGCM rainfall
In this section we investigate further the relationship through the regression analysis analysis as shown in Fig. 12a.In Fig. 12b almost the whole ITCZ during JJA showed negative correlation.The Ethiopian Highland showed the highest negative correlation in Africa.
The same analysis was performed on the ensemble mean for the two seasons (DJF and JJA) as shown in Fig. 13.The model results agree with the observational based regression, showing no effect on the rainfall during the low flow season in the upper catchment of the Blue Nile as shown in the top panel of Fig. 10.The model manages to simulate during DJF the positive signal in the equatorial Pacific and east Africa as shown in Fig. 13a.The model also shows positive signal in the Congo Basin.It captured to a less extend the negative signal in the Amazon Basin and the west most of the equatorial Pacific (Fig. 13a).The lower panel of Fig. 10 shows a negative signal of the regression in Ethiopian Highland during JJA (around −1).However the band and the length of the negative regression is small compare to the observational based results in Africa.The model manages also in Fig. 13b to capture the negative signal in the Indian peninsula.Figure 11 shows the same analysis in Fig. 10.It shows a magnified picture for North Africa for the regression of Nino 3.4 onto the 9 averaged members' rainfall in North Africa for DJF and JJA.Part of Kenya, and part of Uganda and the Congo Basin showed positive signals during DJF as shown in Fig. 11a, the Ethiopian highland and central Sudan showed negatives signals.

Conclusions
In this study we use the tropical band version of the RegCM4 system (Giorgi et al., 2012;Coppola et al., 2012) to simulate the observed statistical relationship between ENSO and the hydrology of the Blue Nile.Towards this objective a series of 9 28-yr long simulations were completed for a domain covering the entire tropical belt between ∼45 • S and ∼45 The RegTB is first evaluated against observations and the reanalysis product over the East Africa region.It is shown that the model performs reasonably well in reproducing the observed climatology of temperature, rainfall, outgoing long wave radiation and large scale atmospheric circulation features.For example, the model captures well the rain belt, as well as the peaks in Ethiopian Highlands, Guinea Highlands, and Cameron Highlands.In general, the temperature biases are approximately between −2 and 2 • C. In fact, this simulation outperforms in some aspects the previous application of this model over the region (Sylla et al., 2010b;Zaroug et al., 2013).In addition, the lower level and large-scale circulation features affecting the monsoon (TEJ, AEJ) are realistically captured.
We then analyse the ability of the model to reproduce the observed connections between Pacific SST and precipitation over the upper Nile River catchment.The model (average of 9 members) was able to reproduce the observed negative correlation between Nino 3.4 SST and upper Nile precipitation, and showed the highest correlation during AMJ (−0.62), in line with previous observational studies (Zaroug et al., 2013).It suggests that AMJ SSTs over the Nino 3.4 region can be a useful predictor in seasonal (JJAS) precipitation forecasting over the East Afrca and Blue Nile river region.Our analysis provides encouraging indications towards the use of the RegTB configuration in seasonal forecasting, climate change and teleconnection studies over thee broas East Africa region.Introduction

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Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | land surface package.As mentioned, the model uses forcing lateral boundary conditions only in the northern and southern boundaries of the domain, with no external forcing in an east−west direction.The initial and lateral boundary conditions are provided by Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Fig
Fig. 5a for the ERA Interim reanalysis and Fig. 5b for the RegTB simulation.Direct observations of the low level wind over the region are not available, and thus we use here the reanalysis product for evaluating the model.Overall, the model reproduces well the main features of the low level circulation, such as the low level monsoon flow over North Africa, the northerly Harmattan flow over East Africa, and the southeasterly flow over the Horn of Africa.The TEJ develops between 200 and 150 mb in the upper troposphere over India in response to a large meridional thermal gradient and settles during the northern summer Asian monsoon season between the Tibetan Highlands and the Indian Ocean(Fontaine and Janicot, 1992;Koteswaram, 1958;Chen and van Loon, 1987).
Discussion Paper | Discussion Paper | Discussion Paper |

Figure 7
Figure 7 provides an assessment of the capability of the observational GPCP dataset to captures the differences in East African climate between La Niña and El Niño years.

Figure 9
Figure9shows the correlation between the ensemble average rainfall anomalies over the upper Nile river catchment (see box in Fig.1) during JJAS and the SST anomalies in the Nino 3.4 region during three month periods from January-February-March (JFM) to October-November-December (OND).The correlations are negative for all SST 2241 between SST in the Nino 3.4 region for different seasons (DJF and JJA), and the GPCP and model rainfall.The upper panel of Fig. 8 presents the regression of DJF Nino 3.4 index onto the DJF GPCP rainfall from 1982 to 2009, while the lower panel of Fig. 8 shows the same regression for JJA.Nino 3.4 has an impact on many regions around the world.During DJF it has a high positive signal in the equatorial Pacific as shown in Fig. 11a, Kenya and Tanzania shows also to a less extend a positive signal.On the other hand, the southern countries of Africa, Amazon Basin and western equatorial Pacific shows negative signal.In Fig. 11b during JJA, the positive signal in the equatorial Pacific becomes narrower.The negative signal extends along the Sahel region and below up to the equator.The India, and south east Asia and northern part of South America shows a negative signal.The same regression analysis focusing on the East Africa region is shown in Fig. 12.We find a positive signal over Kenya showed during DJF a positive signals of regression Discussion Paper | Discussion Paper | Discussion Paper | • N, driven by observed SST and north-south boundary conditions from the ERA-Interim reanalysis.Discussion Paper | Discussion Paper | Discussion Paper |